退化环境下的FMCW激光雷达Teach and Repeat导航方法
Degeneracy-Resilient Teach and Repeat for Geometrically Challenging Environments Using FMCW Lidar
AI总结：
本文提出一种退化鲁棒的FMCW激光雷达Teach and Repeat导航系统，通过多普勒速度里程计和退化感知定位方法，提升在几何退化环境中的自主导航能力。
AI中文摘要：
Teach and Repeat（T&R）拓扑导航使机器人能够自主重复已遍历的路径，而无需依赖GPS，使其在GPS拒止环境如地下矿井和月球导航中具有优势。最先进的T&R系统通常依赖于迭代最近点（ICP）估计；然而，在几何退化环境中，地形稀疏，ICP常变得病态，导致定位降级和导航性能不可靠。为解决这一挑战，我们提出了一种退化鲁棒的频率调制连续波（FMCW）激光雷达T&R导航系统，包含多普勒速度基于的里程计和退化感知的扫描到地图定位。利用FMCW激光雷达，通过多普勒效应提供每点径向速度测量，我们将几何无关、对应无关的运动估计扩展到包含原理化的姿态不确定性估计，使其在退化环境中保持稳定。我们进一步提出一种退化感知的定位方法，结合每点曲率以改进数据关联，并统一平移和旋转尺度以实现一致的退化检测。在三个结构丰富程度不同的环境中进行闭环现场实验，证明所提系统能够可靠地完成自主导航，包括在具有挑战性的平坦机场测试场中，传统ICP系统失败。
英文摘要：
Teach and Repeat (T&R) topometric navigation enables robots to autonomously repeat previously traversed paths without relying on GPS, making it well suited for operations in GPS-denied environments such as underground mines and lunar navigation. State-of-the-art T&R systems typically rely on iterative closest point (ICP)-based estimation; however, in geometrically degenerate environments with sparsely structured terrain, ICP often becomes ill-conditioned, resulting in degraded localization and unreliable navigation performance. To address this challenge, we present a degeneracy-resilient Frequency-Modulated Continuous-Wave (FMCW) lidar T&R navigation system consisting of Doppler velocity-based odometry and degeneracy-aware scan-to-map localization. Leveraging FMCW lidar, which provides per-point radial velocity measurements via the Doppler effect, we extend a geometry-independent, correspondence-free motion estimation to include principled pose uncertainty estimation that remains stable in degenerate environments. We further propose a curvature-enhanced degeneracy-aware localization method that leverages per-point curvature for improved data association and adaptive registration formulation, and unifies translational and rotational scales to enable consistent degeneracy detection. Closed-loop field experiments spanning environments with varying structural richness demonstrate that the proposed system reliably completes autonomous navigation, including in a challenging flat airport test field where a conventional ICP-based system fails. We release the implementation of this work at: https://opensource_code/place_holder.
